{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第五周作业\n",
    "利用Logistic回归技术实现糖尿病发病预测\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 1. 对数据做数据探索分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##   import 工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#首先 import 必要的模块\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "##  读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"pima-indians-diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查数据规模\n",
    "读取测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Train :', (768, 9))\n"
     ]
    }
   ],
   "source": [
    "print(\"Train :\", train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据基本信息\n",
    "#total values in each column, null/not null, datatype, memory occupied etc\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该数据集已知存在缺失值，某些列中存在的缺失值被标记为0。通过这些列中指标的定义和相应领域的常识可以证实上述观点，譬如体重指数和血压两列中的0作为指标数值来说是无意义的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数值型特征的基本统计量\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从结果中我们可以看到很多列的最小值为0。而在一些特定列代表的变量中，0值并没有意义，这就表名该值无效或为缺失值。\n",
    "\n",
    "具体来说，下列变量的最小值为0时数据无意义：\n",
    "1、血浆葡萄糖浓度\n",
    "2、舒张压\n",
    "3、肱三头肌皮褶厚度\n",
    "4、餐后血清胰岛素\n",
    "5、体重指数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第1、2、5列中0值较少；相比较而言，第3、4列中的0值多出数倍，接近总量的一半。\n",
    "为了确保有足够的数据量来训练模型，针对不同的列需要有不同的缺失值判断策略。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "##  查看每个变量的分布 及其与标签之间的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/rM/CJEmj1eUI4sXD20kuBM7qrSJJ0ljochXTs1TVJ/AeCEma8bqcYrpoaHMOsIJnTjlJ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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#该问题为分类问题，类别型特征直方图可用countplot\n",
    "sns.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 怀孕次数pregnants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OHshAzMysvZR5imk0aYKfUbXHe7hvM7PBrcxTTD8HLiX1nl7Z3HDMzKxdlEkQb0XE/216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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "### Number of occurrences\n",
    "sns.countplot(train['pregnants'])\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#怀孕次数有超过17的？\n",
    "#但在疾病判断案例中，异常值可能就意味着得病，不能删除\n",
    "ulimit = 10\n",
    "\n",
    "#删除怀孕次数大于10的样本\n",
    "train = train[train['pregnants'] < ulimit]\n",
    "print (train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2537cdd8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"pregnants\", hue=\"Target\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "怀孕次数和是否得病好像还真有关系！！！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plasma_glucose_concentration\n",
    "血浆葡萄糖浓度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CkCT1GmaoDUlacbx3wjMISdI8TBCSpF4TTxBJ7pHkU0kuT3JZkpNb+QFJzk1yRXvef9Kx\nSZJuNY0ziJuA36qq/wH8NPD8JEcApwAbqupwYENblyRNycQTRFVtraoL2/KNwOXAwXRzTKxvm60H\njpl0bJKkW021DyLJWuCBwEbgoKraCl0SAe42zz4nJdmUZNP27dsnFaokrThTSxBJ9gPeD7yoqoYe\nuqOqTq2qdVW1bvXq1eMLUJJWuKncB5HkdnTJ4fSq+kArvjbJmqrammQNsG0asUnSUi3l3gmY3fsn\npvErpgBvAy6vqjcO/Ols4IS2fAJw1qRjkyTdahpnEA8HngVckuTiVvZ7wGuBM5OcCFwFHDeF2DRj\nlvqNTNLoJp4gquqzQOb585GTjEWSND/HYpKkKZvVcZ8cakOS1MsEIUnqZYKQJPUyQUiSepkgJEm9\nTBCSpF4mCElSLxOEJKmXCUKS1MsEIUnqZYKQJPUyQUiSepkgJEm9TBCSpF4mCElSLxOEJKmXCUKS\n1MsEIUnqZYKQJPUyQUiSeq2adgBaOZYyMbuk6fEMQpLUywQhSeplgpAk9VrRfRBLuSb+jIceMoZI\nJGn2eAYhSeplgpAk9TJBSJJ6reg+CC2d9zRIy59nEJKkXjOXIJL8YpIvJ/lqklOmHY8krVQzlSCS\n7An8FfA44Ajg6UmOmG5UkrQyzVofxE8BX62qKwGSnAEcDXxxqlEtc/YnSOozU2cQwMHA1QPr17Qy\nSdKEzdoZRHrK6jYbJCcBJ7XVbyf58gj1HQh8c2d2OH6EynbCTsc1Ica1c2Y1Lpjd2IxrSO2zaKlx\n/fdhNpq1BHENcI+B9bsD3xjcoKpOBU7dFZUl2VRV63bFsXYl49o5xrXzZjU249o5445r1i4xfR44\nPMmhSW4PPA04e8oxSdKKNFNnEFV1U5IXAJ8A9gTeXlWXTTksSVqRZipBAFTVx4CPTai6XXKpagyM\na+cY186b1diMa+eMNa5U1eJbSZJWnFnrg5AkzYgVmSBmZTiPJPdI8qkklye5LMnJrfwPkvxrkovb\n4/FTiG1Lkkta/Zta2QFJzk1yRXvefwpx3XugXS5OckOSF02jzZK8Pcm2JJcOlM3bRkle0t5zX07y\nCxOO6/VJvpTkC0k+mOQurXxtku8NtNubxhXXArHN+9pNuc3eMxDTliQXt/KJtdkCnxGTeZ9V1Yp6\n0HV+fw04DLg9sBk4YkqxrAEe1JbvCHyFboiRPwBePOV22gIcuEPZnwCntOVTgNfNwGv5b3S/6Z54\nmwGPAB4EXLpYG7XXdTOwF3Boew/uOcG4fh5Y1ZZfNxDX2sHtptRmva/dtNtsh7+/AXj5pNtsgc+I\nibzPVuIZxI+G86iqHwJzw3lMXFVtraoL2/KNwOXM9p3jRwPr2/J64JgpxgJwJPC1qvqXaVReVZ8G\n/n2H4vna6GjgjKr6QVV9Hfgq3XtxInFV1TlVdVNb/X909xhN3DxtNp+pttmcJAGeCrx7HHUvZIHP\niIm8z1ZigpjJ4TySrAUeCGxsRS9olwPePo1LOXR3sJ+T5IJ29zrAQVW1Fbo3LnC3KcQ16Gnc9j/t\ntNsM5m+jWXrfPRf4+4H1Q5NclOT8JD83pZj6XrtZabOfA66tqisGyibeZjt8RkzkfbYSE8Siw3lM\nWpL9gPcDL6qqG4C/Ae4JPADYSnd6O2kPr6oH0Y2s+/wkj5hCDPNKdyPlUcB7W9EstNlCZuJ9l+Sl\nwE3A6a1oK3BIVT0Q+E3gXUnuNOGw5nvtZqLNgKdz2y8iE2+zns+IeTftKVtym63EBLHocB6TlOR2\ndC/86VX1AYCquraqbq6qW4C3MKbT6oVU1Tfa8zbggy2Ga5OsaXGvAbZNOq4BjwMurKprYTbarJmv\njab+vktyAvBE4PhqF6zbpYjr2vIFdNes7zXJuBZ47WahzVYBxwLvmSubdJv1fUYwoffZSkwQMzOc\nR7u2+Tbg8qp640D5moHNngxcuuO+Y45r3yR3nFum6+C8lK6dTmibnQCcNcm4dnCbb3XTbrMB87XR\n2cDTkuyV5FDgcOBzkwoqyS8CvwscVVXfHShfnW4eFpIc1uK6clJxtXrne+2m2mbNY4EvVdU1cwWT\nbLP5PiOY1PtsEj3xs/YAHk/3a4CvAS+dYhw/S3f69wXg4vZ4PPC3wCWt/GxgzYTjOozulxCbgcvm\n2gi4K7ABuKI9HzCldrsDcB1w54GyibcZXYLaCvwn3Te3ExdqI+Cl7T33ZeBxE47rq3TXpufeZ29q\n2/5Se403AxcCT5pCm8372k2zzVr5acDzdth2Ym22wGfERN5n3kktSeq1Ei8xSZKGYIKQJPUyQUiS\nepkgJEm9TBCSpF4mCElSLxOEhpbk5ja88aVJ3pvkDq3829OObTFJTkvylGnHMcuSPDvJjy1hv2OS\nHDGw/odJHrtro9M0mCC0M75XVQ+oqvsCPwSeN+2AtEs9G+hNEHN3Ds/jGLphpgGoqpdX1Sd3bWia\nBhOEluozwI8PFiTZL8mGJBemm2zo6Fa+b5KPJtnczj5+uZVvSfLHSf45yaYkD0ryiSRfS/K8hY45\nnyQvSzcxzrlJ3p3kxT3bbElyYFtel+S8gbre0er5QpJfauVPb2WXJnldK9uznZVc2v72G638nkk+\n3kbB/UySn1gg1oPSTd6zuT0e1sp/sx330iQvamVr000a85Z0E8eck2Sf9rcfT/LJdowLk9yzlf92\nks+3f8srFzpOO7taB5zezhL3ae308iSfBY5L8ivteJuTvD/JHVrMRwGvb/vdc/BsLcmR6UY9vSTd\nSK17DbwGrxx4XedtJ03ROG+r97G8HsC32/MqurFffq2n/E5t+UC64R1CNzTBWwaOc+f2vGXgGP+H\nbjiBOwKrgW0LHXOe+NbRDUWwTzvOFbSJaOiGTHjKQL0HDuxzXlt+HfBnA8fbn+4b9VUtplXAP9B9\nY34wcO7AtndpzxuAw9vyQ4F/WKA930M3Oid0kx/duR33EmBfYD+6IR0eSDdJzU3AA9r2ZwLPbMsb\ngSe35b3phiL5eboJ7UP3RfAjdJPiLHSc84B1A/FtAX5nYP2uA8t/BLxwx7YdXG+xXA3cq5W/c+Df\nu2Vg//8NvHXa728f//XhGYR2xj7ppl3cRPeh+bYd/h7gj5N8Afgk3Tj0B9F94D02yeuS/FxVfWtg\nn7mBEi8BNlbVjVW1Hfh+umkx5ztmn58Fzqqq71U3ucqHd/Lf91jgr+ZWqup64CF0CWR7dRPunE73\nQXslcFiSv0g3EN4N6YZkfhjw3tZOb6abEWw+j6Eb6prqRjP9Vvs3fLCqvlNV3wY+QDcfAcDXq+ri\ntnwBsDbdoIoHV9UH23G+X91gfD/fHhfRjRf0E3QDt/UeZ4EY3zOwfN92VnQJcDxwnwX2A7h3q+sr\nbX09XdvNmRuZdLEYNCWrph2Adivfq6oHLPD34+m+aT+4qv4zyRZg76r6SpIH0w0y9pok51TVH7Z9\nftCebxlYnltfNd8x56m/byz8Pjdx6+XVwWOF/zp2fu8xq+r6JPcHfgF4Pt2MYy8C/mORNlrMQv+G\nwfa5me5Mab7tA7ymqt58m8Ju0pm+48znOwPLpwHHVNXmJM8GHrXAfnMxLGQujpvxs2gmeQahXenO\ndJeG/jPJo+nmiibdL2O+W1V/B/wp3dy/Ix1zHp8FnpRk7/Zt/gnzbLeF7lIOdJe/5pwDvGBuJd3M\nZhuBRyY5MF1H7dOB81sfxh5V9X7gZXTzBt8AfD3JcW3/tCQynw3Ar7Vt90w36cyngWPa9f196Ya/\n/sx8B2h1XpPkmHacvdL9uuwTwHNbO5Dk4CSLzQB4I92lufncEdiabn6C44fY70t0ZzlzfVXPAs5f\nJAbNEBOEdqXTgXVJNtF9gHypld8P+Fy77PJSuuvXox7zv6iqz9NdstpMd/liE/Ctnk1fCfx5ks/Q\nfXud80fA/q1zeDPw6Oqmc3wJ8Kl23Aur6iy6S13ntX/TaW0bWowntv0vY+H5zk8GHt0u2VwA3Ke6\n+YdPoxvDfyPdtfmLFjgGdB+8v94uw/0T8N+q6hzgXcA/t+O/j4U//Gn1vmmuk7rn7y9rMZ3LbV+H\nM4Dfbp3R95wrrKrvA8+hu+R2Cd1Z4ZsWiUEzxOG+tawk2a+qvt2+RX8aOKl96EraSV7303Jzarqb\ntvYG1pscpKXzDEK7nSRzs2nt6MhqcwXPkiQvBY7bofi9VfXqacQjDcsEIUnqZSe1JKmXCUKS1MsE\nIUnqZYKQJPUyQUiSev1/LBdqkoKY0QcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Plasma_glucose_concentration, kde = False)\n",
    "plt.xlabel('Plasma_glucose_concentration')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eRTp5nXI5+ODojJm2xUlq/hlwqqo+rqpvqurjwOnAZbEJLbG88cYbfPDBB9T1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\nwhhjTJssWRhjjGmTJQtjjDFt+v8YTTY7Lzv9pAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Plasma_glucose_concentration', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Plasma_glucose_concentration', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### blood_pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FXDWhze2RXVUD2xv6APZj2tiP6TJIP1JVC68lSVpypu0YhCRpSizJgEjymiR3JbknyTlD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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.blood_pressure, kde = False)\n",
    "plt.xlabel('blood_pressure')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "血压为0？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看blood_pressure与标签之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fjNmqWigiCcAJwE8ILSfSrraB69SpEwBS59wHpXp8zJ49G1XlrbfeQj3R2Uby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\npsUsaRhjjGmx/weCfqWRYHSY4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='blood_pressure', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('blood_pressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Triceps_skin_fold_thickness\n",
    "三头肌皮褶厚度（单位：mm）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4qqpWAVf1jyfRm4HbBx5Per//Grisqv498Mt0fZ/oPidZAZwMrK6q59Cd2LKGyez3R4Cj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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Triceps_skin_fold_thickness, kde = False)\n",
    "plt.xlabel('Triceps_skin_fold_thickness')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like there are some outliers in this feature. So let us remove them and then plot again.\n",
    "但在疾病判断案例中，异常值可能就意味着得病，不能删除\n",
    "\n",
    "ulimit = 80\n",
    "train = train[train['Triceps_skin_fold_thickness'] < ulimit]\n",
    "\n",
    "plt.scatter(range(train.shape[0]), train[\"Triceps_skin_fold_thickness\"].values,color='purple')\n",
    "plt.title(\"Distribution of Triceps_skin_fold_thickness\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y1qxZTYlnraZQozFJFHc9iS1bt3H//Q9QURE6M1eNaajCwkJGPfwQyVEubjmo\ngMgAtf5O+z2OjYXhbCwMZ9ziJKb9Hpg/ElOildsOzqegII+xY8bgcgV23oi3M7jPreG4v/oyQpqq\n8txzz7Fw4ULKOh5Zr+XGA8WV2I7STsexbNlSxo0bZ0NqTZMxefJkduzYwc2980kI0BLjAJuKIih1\nhVHqCmN1XiSbirwdVNpwGQkuLu9exC+LF/Phh4Ftnfc2Fz8lIqdWPyAijwLn+z6k0Dd16lQ++OAD\nKtodhKNdn2CHUydny66UpR/OggULmDBhQqNYDtmY2qxatYq5c+dyesdSuiWH5hIe/nJCWjl9Uh28\n9uor5OXlBaxcb5PFWcArVZ3eIvIE7ol0J/orsFD13nvv8cYbb+Bo2a1B+1IEmqPdQVS0O4gPP/yQ\nV199te43GBPCXnn5ZVKiYUin0F2l1V9E4LLuxZSUlAR0EzRv9+BeAZwLvO0Z2noCcKKqhubyiH4y\ne/ZsJk2ahLPFAZR1Po6gr3G8P0QoTz98zxyMadP8NTbBGP/aunUrvyxezEntS4gNXAtQSOkQ7+KQ\nlhV88vHsgC3vU9vQ2b2HyMbiXpLjZGAM0K+hQ2cbk3nz5vHU00/jTE6ntMvfgjuXor5EKD/gGBwt\nu/LKK6/w3nvvBTsiY/bbl19+CcDxaeVBjiS4jm9fRl5BIUuWLAlIebXl5ek1HHcCz3seKxD8dTb8\n7Ntvv+Wxxx5zdxZ3OwnCArwPoy9JGGWdByKVTiZNmkR8fDxnnHFGsKMyxmtr166ldZx7e9LmrIen\nr2bt2rUccYT/V42oMVmoake/l94I/Pbbbzz88CM4Y1u6J92FNYF6r4RR2uVvxK39nCeeeILk5GSO\nOeaYYEdljFeyNm8mLcaGgcdHKsnRErBJt/VqSxGRgSJytK+DCTVbt27l3vvuwxkZR0n3U/w+Ozt6\n0w+El+QQXpJD7Oo5RG/6wX+FhYVT0vUkXHEteeSRUWRmZvqvLGN8KCoqCpcN6APApe7PIxC8nWex\nQEQGeh7fBbwPvC8iI/wZXDCVl5fz4EMPUVxaQVG3U9DIGL+XGVaSi7gciMtBROF2wkr8PBcxPJKS\n7oOokAgefOghioqK/FueMT6QmJREnqMJ1PAbqNwFJQ4lMTGx7pN9wNuaxcG4d8oDuBH4G3AkcJMf\nYgoJ06ZNY93atRR3GujTvbNDjUbGUdL5b2zfvp0XX3wx2OEYU6e+ffuypSiM3PJGOMjEh1bvjqRS\nCdhCod5+2mFApYh0ASJUdYWqbgJS/Rda8Gzfvp233noLR8uuuFo0+f57XIltqWjTh9mzZ1tzlAl5\nVauu/pAdmOaXUPV9djQx0dEOT+jfAAAfgElEQVT07ds3IOV5myy+w71J0RPAfwE8iaNJzrN45513\nqFQoTx8Q7FACprx9PyQi2uZfmJDXpUsXDjv0UOZsjqeseU3e3mNLcTjfZ0dzzt//Hlp9FsBVQBmw\nBnjIc6w38JwfYgoqp9PJvM8/pyIlA42KD3Y4gRMRTXlqV7799juKi4uDHY0xtbrm2mspKIf3N4TQ\nSs8BUqnwf5kJxMREc9FFFwWsXG9ncO9U1XtU9X5VLfIcm62qT1edIyIhu+fE/tiwYQNFhYU4U5p+\n89PenC0ycDodrFy5MtihGFOrPn36MGTIEOZujmV5bgjsIRNAczfHsDI3gpuH3UJKSkrAyvVlD1GT\nWCdq06ZNAFTGNcnumFpVxrp/5j/++CO4gRjjhZtuuokDMjrywsokskuaR2f38txI/rMunoEDB3Lm\nmWcGtOzm8Qnvh6pNgjQiOsiRBF7Vz2xDaE1jEBMTw9hxjyHRCTy5LIWCisCs1VbqFGJiYjjvvPOI\niYmh1BmYcv8oDGfi8iQO6NSJESNGIAFem86SxV4iIjzjt5vjMt7qXj4hPLwRL2dimpX09HQeG/84\n+c4oHl8amIRR4hTOOusshg0bxplnnklJAJLF5qJwnlqWQmJKS5548ikSEhL8XubeLFnsJSnJPadC\nHKVBjiTwxFkGQHJycpAjMcZ7ffr0Yey4x8gui2b8rynk+zlhxEUos2fP5rnnnuPjjz8mLsK/f1hu\nKgznsV9TiEpI5Zl/P0urVsHZldOXyaIRrddds3bt2gEQVt789qyu+pmrPgNjGosBAwYw/vHH2VkR\nzdglLdhZ6r+/g2MjlLKyMmbOnElZWRmxfkwWa/IiGPdrCrFJLZkw8Tk6dgzekn2+/EQf9+G1giYj\nIwMRIax0d7BDCbiqn7lTp07BDcSYejj00EN56ulnKCaBRxe3YFNh425O/WVnJE/8mkzLth14btJk\nOnToENR4vF0bariI9Pc8PkJE1ovI7yJyZNU5qjrGX0EGUkxMDB06pBNe0iTnG9YqrDiH+IRE2rRp\nE+xQjKmXgw8+mImTJhGZmMrYJSn8ltM4h9V+nhXNxOVJdOvRk+cmTQ6J2r63NYs7gT88j8cDk4Gn\ngQl+iCno+vTpTWTJrmbXyR1Zsos+vQ8M+CgLY3ypc+fOTH7+RdI6duLpZUl8tbXxjGysVHgrM443\nf0/g6KOO5pl/PxvQuRS18TZZpKhqnogkAP2BZ1X1JaCX/0ILnt69e6MVpUhz6rdwliMlufTp0yfY\nkRjTYG3atOG5SZM5bMAAXl2dwH/WxVIZ4n/7lbvgueWJzN0cyz/+8Q8eHTOG2NjYYIe1h7fJIsvT\n5HQB8I2qukQkEXA1pHAR6SgiX4rIKhFZISLDPcdTRWSeiGR67ls0pJz91b9/fwAiCrcFstigCi/M\nBgK3gqUx/hYfH89jj43nzDPP5KONcby4IoGKBn1j+U9+hfDYkhQW74pi2LBh3HrrrSE3hN3bZHEP\n8BEwGvf+2wBnAT83sHwncKeqHggcBdwsIr2BkcB8Ve0OzPc8D5iMjAxSW7YkPH9LIIsFV8WfJvvg\nCtxuYBH5WURFR9O7d++AlWmMv0VERHDXXXdxww038MOOaJ5cmkyRI7SaWbcVhzF6cQu2lMfy6KNj\nOO+884Id0j55uzbUbFVto6rpqlqVIP4LnNOQwlV1m6ou9jwuBFYBHYChwFTPaVMbWs7+EhGOO/ZY\nogq2gMsRuHKdFX+a7CPOACULrSQ6fxNHHnFEwFawNCZQRIRLLrmEhx56iPVF0YxZ0oJdfhxauz8y\n8yN4dEkLHBFJTJgwkeOOOy7YIdXI609MRLqIyAgRmeDZIa+Dqpb5KhAR6QQcAvwItFXVbeBOKEDA\nh+cMGjQIdTmI2P1HwMrUiKg/TfbRiMB8cYfnb0ErShg0aFBAyjMmGE466SSefOppCjSeR5e0YEtx\ncJt5lu6KZPyvySS3SmPyCy/Sq1dodwF7O3T2QuA34Ajc/RSHA0s9xxvM03E+E7hNVQv24303iMgi\nEVm0c+dOX4Syx8EHH0zGAQcQk70icKOiwqP+NNmH8MAki+js5bRIbckxxxwTkPKMCZb+/fsz8blJ\nSGwKY5eksL4gOAnjh+wonv0tiU6duzL5+ReCPofCG97WLMYBZ6nquap6h6qeh7vPYnxDAxCRSNyJ\nYrqqvu85nC0iaZ7X04Ad+3qvqk5R1QGqOqB169YNDWXvuLj8ssuQklwictb59NqhJDw/i/CCbVxy\n8UVERjbOMenG7I8uXbrw3KTJJLZow+NLU1hXENj9vL/bHsULKxLpc/DB/PvZCSEzNLYu3iaLZGDh\nXse+BRq0U7i4B/S/CqxS1WeqvTQLuNLz+EogKHtlnHzyyfTo2ZO4LT83zbWiXA7iNv1Au7Q0hg4d\nGuxojAmYDh06MOG5SaS0bMuTS5PZEKAaxo/ZUby0KpF+/frxxBNPBmVBwPryNllMAB4VkRgAEYnG\nPTLq2QaWfyxwOXCSiPzquQ3GXWM5RUQygVPwQQ2mPsLCwhg5YgRhlQ5iNnyzZ1XWJkGVmI3fQXkh\n944caR3bptlp06YN/352AoktWvPUshR2+LnTe0VuBC+uTOTggw7isfHj3SMeGxFvP51rcM/izhOR\nLUA+cBdwjWfpj/Uisn5/C1fVhaoqqtpXVft7bnNUNUdVT1bV7p773P29tq906dKF4bfeSkR+FtGb\nf2oys7qjti0jMmcdV191lc2tMM1Wu3bteOrpZyAqnmeWpVDsp2G1W4rDeW5FMh0zMhj32PiQmmzn\nLW8b667zaxQhbsiQIWzcuJGZM2ei4VFUtD8EGvGSGJHZK4ne8gsnn3wyV1xxRbDDMSaoOnbsyKNj\nxnLXnXfy8qoEhh9c6NNf73IXTFyeTHR8MuMff6JRNT1V51WyUNX5/g4k1N18880UFxczd+5cpNJJ\nefrhjTJhRG1bRnTWIo4+5hjuvfdeWwfKGNyjpK6/4QZeeOEFvt4WzQnty3127XfWxrOtWHj66YdC\nYkHA+vJ26GyUiIzyrDSb6zl2ioj8y7/hhY6wsDDuuecezjnnHKK2Lydm/ddQGaJrB+yLVhK98Qei\nsxZx4oknMXrUqP/tCmiM4fzzz+eQ/v14a12Cz5qjNhSE8/kW96oMhx12mE+uGSze9lk8AxwGXFvt\nPauAm/0RVKgKCwtj+PDhXHfddUTmriP+97mNY5SUs5zYzM+J2rGSCy64gAceuN+GyRqzl7CwMG66\neRilDpiX5ZvO5w//iCMhPo6rr77aJ9cLJm+TxbnARar6DVAJoKpZQLq/AgtVIsJll13Gww8/THT5\nbhJWfURYkW8nBPpSWGkeiatnE1W4jdtvv52bbrop5BYoMyZUdO/enWOOPprPt8Y1eCxLTlkYi3dF\nce555xMfH++bAIPI22Th2PtcEWkFBG2UUrCdeOKJPD95Mq2T44hfM4eInb8HO6S/iNj9BwmrPiIp\nSvj3v5+xuRTGeOHY446joBy2ljTsj6o1ee5m3lBe72l/eJss3gNeF5GOACLSGpgIvOOvwBqD7t27\n8/KUKRzavx+xfywkeuP3UBkCczFUidqymNi1X9CjW1deeXmKDY81xktVazRtLmpYsthUFEFEeDhd\nunTxRVhB522yuBfYBvwOpACbgBzgEf+E1XikpKTwxBNPcOGFFxK1YxVxmZ+B03cjKfaby0nsui+J\n3vorp59+OhMnTrBtUo3ZDw6He6XpqLCGtUNFhSlOVyMaBFMHb5coL1fVYUAc7iXE41X1FlUN4rdi\n6IiIiOBf//oX9957L1HF2SSsmROcjm9nBfG/zyUibyM333wzI0aMIDq68WwpaUwo2Lx5MwDJUQ1L\nFinRlX+6XmPn7dDZS0Wkr7ptU9VKEekrIpf4O8DG5LTTTuPJJ58k2llCwppPEIfPVnCvm8tBfOan\nRJbmMOqRRzj//PNtDoUx9TDrww9oE6d0TnI26DqHta4gPAxmzZrlo8iCa39Wnc3a69gWz3FTzaGH\nHsqTTz5BhKOY2PULArOelCoxfywkvHgXo0aN4oQTTvB/mcY0QfPnz+e35SsY1L6EsAb+rZUcpRzZ\nupzZH83i999DbwDM/tqfVWfz9jqWBwR0b+zGol+/ftx223DCC7YSkbvB7+WFF2wlMncDV199dZMZ\neWFMoGVmZvLE4+PpmeJkULpvWgUu6V5MQriTB+6/j7y8vb9CGxdvk8Uq4O97HRsCrPZtOE3H4MGD\n6ZhxANE7/f8RRe5cQ0qLFlx00UV+L8uYpmjp0qXccfttxIc5GHZQARE+WoA2KUoZflAeu3N3cest\nw9iyZYtvLhwE3n4kI3APnX1HRMaJyLvAG8DdfouskQsLC6P3gb0Iryj2e1nhFUV079bNlhk3ph4+\n+eQT7rzjDhK0iHv7725wx/beOie5uLtvPrnZWfzrnzeydOlSn14/ULwdDfU10A/31qotgWVAP89x\nsw9Op5Nly37DGd2g/aG84opOYuWq1e6tWI0xXsnLy2P06FE8/vjj9Egu56FDd9M2zj99jL1aOHno\n0N3EuQq5/bbbmDJlCuXljWswqdeVLVXdoKpjVPVGz/0ffoyrUausrGTChAls27aVitYH+r08R5te\nFBcVMm7cOJzOho3gMKapU1W++uorrrryCr5a8CX/6FzCXX3ziY/071417eIqeWTAbo5rV8pbb73F\nDddfx6pVq/xapi/VuOyoiDyvqjd5Hr8O7POTVNVr/BRbo5Sfn8+4cY/x448/UN7uYJypnfxepiux\nHWUdj+Drr7/mtttv56EHH7SJeMbsw8aNG5k06Tl+/nkRByRWcteAAjISAjdxLi5Cue7AYo5oU8Fr\nazZz003/YvDgM7n++utDfi/u2tao3lrt8d7DZs1enE4ns2fP5tXXXqewqIiyjKNwtPF/raKKo91B\naEQMK1Z+z5VXXsXll1/Gueeea5PyjAGKi4t58803ee+9/xAVVskl3YsZ1KHMZx3Z+6tvSwfjjsjl\ngw2xzJ3zMQu+/IJrrr2OoUOHhuzWATVGpapjAEQkHMgE3lVVaxTfS0VFBZ9//jnTpk9n65Yt7r/y\nD/wblXEtAx6Ls1U3ChNa49j8E1OmTGHm+//l0ksuZvDgwY1uv19jfMHlcvHJJ5/wystTyMsv4Pi0\nMs7vWuLzTuz6iItQLulewgnty5me6eC5555j1gf/5eZbbuWII44Idnh/IerFOrwikq+qyQGIp94G\nDBigixYtClh5+fn5zJkzh3f/8x67c3PQ+JaUph2CK6VjvXfQi109h4jC7XueOxPbUdprcL2uFV6w\nlZgtiwkr2kFCYiLn/uMfDBkyhJYtA5/EjAmGZcuWMXHiBNauXUf3ZBeXdi+kS1LDm5zGLU5idd7/\n9oPpleLgvkMLGnRNVVi8K5IZ6xLZUSIcddSR3HzzMDp27NjQcOskIr+o6oC6zvO2vvOxiAxW1TkN\njKvRW7t2Le+//z7z5s3D4XDgSkqjvMepuJI6hNQ2q66k9hQntSe8cDvO7b8xdepUpk2bxgknnMC5\n555L7969bTkQ0yQVFBTwwgsv8Mknn9AyFm7qU8iRbSpC6dfzL0TgsNYO+rbMZV5WDB/+8iPXXL2I\ny6+4kosvvjgkNivzNlmEAe+LyEJgM9U6u5tDB7fT6eSbb75h5vvvs/y335DwCMpTu+JocyCVcanB\nDq9WrsR2lCa2Q8ryidqxii+/XsgXX3xBt+7dOe/ccznxxBOtX8M0GV988QUTJzxLQUEBZ2aUck7n\nEqIb0V5fkWEwOKOMY9qWMz0zntdee435n8/j7ntGcNBBBwU1Nm+boR6t6TVVfdCnEdWTP5qhysrK\nmDNnDm/NmMGunTshJpGy1r1wtOoBEb7/gvVlM1SNXA4ic9YSvWMVUppHckoKF5x/PkOHDiUhIcG3\nZRkTIE6nk0mTJvHBBx/QJcnFNT0LyUj0zygnfzRD1eTXXZFMzUxid3kYt9xyC+ecc47PWwR80gwl\nIher6oxQSQiB4nQ6mTVrFq+/8QaFBQVUJralrNsgXCnpIEEaPuEr4ZE42hyIo3Uvwgu24spezssv\nv8y0adO59NJLuOCCC2wmuGlUCgoKePjhh1iy5FfOyCjlwq4NXwQwVPRv5aBnSi4vrkxgwoQJrF+/\nnuHDhwdlxFRd33wvBSSKGojI6SKyRkTWisjIQJSZmZnJddffwMSJE8kjgZJegynudSauFhmNP1FU\nJ4IruQMlPU6juPcQCqNb8corr3DlVVexbNmyYEdnjFdUlTGPPspvS3/l+gOLuLhb00kUVWIjlOEH\nF3LWAaV89NFHvP7660GJo65vv6B97J4hu5OBM4DewMUi0tufZa5YsYJbhw/nj63ZlHY9iZIep+FK\nbOfPIkNCZXwrSrsPoqTHaWzLLeLOO+/kxx9/DHZYxtRpzpw5/PTzz1zSrYiBaY1r+Yz9ESZwQdcS\njk8rY8ZbbwVl5nddySJcRE4UkZNquvkxtiOAtaq6XlUrgLeBof4qTFUZ/eijlFZGUNTzTPfM61Ae\nPuEHruQOFPU6i4qoJEaNHm1Lh5iQN+Ot6XRLdnJSh6abKKq7pHsJ8ZHKu+++G/Cy62r4igZepeYa\nhgL+2o28A+6RV1WygCOrnyAiNwA3AGRkZDSosJ07d5K9fTvl6QPQ6Obb0auRMZS16Y1s+Ib169fT\no0ePYIdkTI1KiovpHu9sck1PNYmLUFrFuCgpKQl42XUli2JV9VcyqMu+/vn/NHRLVacAU8A9Gqoh\nhaWmppKW1p6tOWtxtOyGRsU15HKNl7Oc6J2rSUpKJj09PdjRGFOryKgosoojUG0eDQGlTiG3PJwO\nQZh3Eco9tllA9emL6fx5vSqfioiI4M477yDKVULiqllE7N7onlYZQJVxqTgT2+25BXoOR3jBVvfP\nXprLbbcNJy6umSZM02hcdvkVZOZH8PW2wM0Vykhw0ivFseeWkRC45tr/rIujoAIuueSSgJVZpa6a\nRTBz9c9AdxHpjHu/74sAv35CAwYMYMpLL/HQww+zee18KuNbUda+P67k+i/hsT/KM47yexl/oUp4\n4Xaity4hvHA7rdu0YdQjj9O7t1/HEhjjE2eeeSbz5n3G1OW/IcDx7f3fd3FZj8A3AVWqO1F8viVm\nzwoMgebVpLxgEZHBwLNAOPCaqo6t6VxfTspzOp18+umnTJ36Jjt2ZENMEuWteuBo1R2NjPVJGUHn\nLCdy11qic35HSnaT0iKVyy+7lLPOOstmdJtGpaCggEceeZjFi5dwanopF3UrCdpqsv5Q7BBeXJnI\n0pxIhgwZwi233OLT5T+8nZQX0slif/hjBrfT6eSrr77iww9nsWzZUpAwnMkdcLTsijMlA8JCcynh\nGlW6iMjPIiJnHVH5WWilk549ezFkyNkMGjTIkoRptJxOJy+88AIzZ86kfbxyRfcCeqc27tF8lQrf\nbo/mnfUJFDvDuPXW4Qwd6vsBoZYsfGzTpk18/PHHzPv8c3JzcpCIKCqSM3C07IIrsT2EheifMlpJ\neGE2Ebnric7biDrKSExKZtDJJzF48GC6d+8e7AiN8Znvv/+eiROeZdv2bI5sU85F3UpoGeOfrVL9\n6Y/CcP4vM4HMvAh69z6Q22+/w2+/q5Ys/MTlcrFs2TLmzZvHlwsWUFpSgkTGUp5yAM6WXXAltA3+\nsAxVwop3EZm7nujdG9CKEqKjYzjuuGM59dRTOeyww0J2gxVjGqq8vJy3336b6dOmoS4HJ3Uo5ewD\nSkkKgT0s6rKtJIz318fx445okpMS+ee/buK0004jzI9/jFqyCICKigp++ukn5s+fz8Jvv8VRUQEx\niZS36IyzZVcqY1sENB4pKyAyZx3Ru9dDaT7hEREcdeRRnHzySRx99NHExjaR/hZjvLB9+3beeOMN\nPvv0U6LC4dT0Ys7oWOb3vbbrY1dZGB9uiOWb7TFERUVx3vkXcOGFF5KYmOj3si1ZBFhJSQkLFy5k\n3rzP+eWXRVRWVlKZ0IaKVt1xpHaBcD+Ni650ErF7I1E7fye8cBsiQt++fTnllFM44YQTAvKfzZhQ\ntnHjRl577TW++uor4iLhjPQSTu1YRmxE8L/7csvCmLUxlq+2xRAWFs6Qoedw6aWXkpoauGHzliyC\nKDc3l88//5yPPprN5s2bkPBIylt2o6JtHzQmySdlSEUxkdkricn5HXWU07ZdO84+6yxOPfVU2rRp\n45MyjGlKMjMzef311/nuu+9IiIIz0os5tWNZUPa7yCsXPtoYy5dbY0HCOWPwYC677DLatm0b8Fgs\nWYQAVWXlypV88MEHfPHFF7gqK3GkHEBFh0Pq3UQl5YVEb1lCZO56BGXgwIGcc8459O/f36/tmsY0\nFatXr+b111/jxx9/IiUazulUxPFp5QEZblvqFD7eFMOnm+NwaBhnnHEGl112GWlpaf4vvAaWLELM\nzp07+eCDD3j//f9SWlZKRcvuVKQf5v28DWc50VuXELVzNZEREQw5+2zOPfdc2rdv79/AjWmiVqxY\nwYsvPM9vy1fQLl45r3MRh7f2z/arzkqYvyWGDzfGU1QBJ510Itdcc21ILKljySJE5eXlMX36dN7/\n73+plAhKOh6JK751re8JK8sjbtP3SEUJgwcP5qqrrqJ169rfY4ypm6ry/fffM+WlF/lj4yb6pDq4\nokcRaXG+G267encEUzMT2VIUxqGHHsKNN/6Tnj17+uz6DWXJIsRt3LiRMWPHkvn7716d37FjBvff\nfx+9evXyc2TGND8ul4uPPvqIKS+9REV5KWdllHB2p1IiG9A0VegQ3s6M45vtMbRt05rht93OMccc\n47ugfcSSRSPgdDr5/vvv61xuOCoqimOOOcZmWBvjZzk5OTz//PPMnz+fzkkubu5TQJvY/a9lrMmL\n4PmVyRQ6wrnwoou4/PLLiYmJ8UPEDWfJwhhj6mnhwoWMf2wcrooSrutZyOFtKrx6nyp8vCmG99bH\nk5aWxiOjRof8KgneJgsbPmOMMXs57rjjePmVVzmga0+eW57IF1vqrtVXKrz5ezzvrotn4PEnMOXl\nV0I+UewPW/PBGGP2IS0tjQkTJvLwww/xxg8/klceRnqCq8bzl+yK5NvtMVx88cXccMMNSLCX/fEx\nSxbGGFOD6OhoHn10DKNHjeKDhQvrPP/yyy/nmmuuaXKJAixZGGNMrSIjIxn96KNs3rwZl6vmmkVM\nTExQJ9f5myULY4ypg4iQkZER7DCCyjq4jTHG1MmShTHGmDpZsjDGGFMnSxbGGGPqZMnCGGNMnSxZ\nGGOMqZMlC2OMMXVqMgsJishOYGOw42hCWgG7gh2EMftg/zd96wBVrXODnCaTLIxvicgib1aiNCbQ\n7P9mcFgzlDHGmDpZsjDGGFMnSxamJlOCHYAxNbD/m0FgfRbGGGPqZDULY4wxdbJkYf5ERE4XkTUi\nslZERgY7HmOqiMhrIrJDRJYHO5bmyJKF2UNEwoHJwBlAb+BiEekd3KiM2eMN4PRgB9FcWbIw1R0B\nrFXV9apaAbwNDA1yTMYAoKpfA7nBjqO5smRhqusAbK72PMtzzBjTzFmyMNXta5d5Gy5njLFkYf4k\nC+hY7Xk6sDVIsRhjQoglC1Pdz0B3EeksIlHARcCsIMdkjAkBlizMHqrqBIYBnwKrgHdVdUVwozLG\nTURmAN8DPUUkS0SuDXZMzYnN4DbGGFMnq1kYY4ypkyULY4wxdbJkYYwxpk6WLIwxxtTJkoUxxpg6\nWbIwphYi8qKIPOjluQtE5Dp/x2RMMFiyMM2aiPwhIqUiUigieSLynYj8U0TCAFT1n6r6aADisERj\nQpolC2PgbFVNBA4AxgMjgFeDG5IxocWShTEeqpqvqrOAC4ErReQgEXlDRMYAiEgLEZktIjtFZLfn\ncfpel+kqIj+JSL6IfCgiqVUviMhRnppLnogsFZG/eY6PBQYCk0SkSEQmeY73EpF5IpLr2ZDqgmrX\nGiwiKz01oi0icpd/Px3T3FmyMGYvqvoT7kUVB+71UhjwOu4aSAZQCkza65wrgGuA9oATmAggIh2A\nj4ExQCpwFzBTRFqr6v3AN8AwVU1Q1WEiEg/MA94C2gAXA8+LSB9POa8CN3pqRAcBX/joxzdmnyxZ\nGLNvW3F/qe+hqjmqOlNVS1S1EBgLnLDX+/5PVZerajHwIHCBZwfCy4A5qjpHVStVdR6wCBhcQ/ln\nAX+o6uuq6lTVxcBM4DzP6w6gt4gkqepuz+vG+I0lC2P2rQN77comInEi8pKIbBSRAuBrIMWTDKpU\n3zxqIxAJtMJdGznf0wSVJyJ5wHFAWg3lHwAcudf5lwLtPK+fizvRbBSRr0Tk6Ib9uMbULiLYARgT\nakTkcNzJYiFwZLWX7gR6Akeq6nYR6Q8s4c+bRlXfDyQDdw1gF+4k8n+qen0Nxe69oudm4CtVPWWf\nJ6v+DAwVkUjcKwW/u1fZxviU1SyM8RCRJBE5C/fe49NU9be9TknE3U+R5+m4fngfl7lMRHqLSBww\nGnhPVV3ANOBsETlNRMJFJEZE/latgzwb6FLtOrOBHiJyuYhEem6Hi8iBIhIlIpeKSLKqOoACwOWz\nD8KYfbBkYQx8JCKFuP+avx94Brh6H+c9C8Tirin8AMzdxzn/B7wBbAdigFsBVHUzMBS4D9jpKetu\n/vc7OAE4zzPKaqKnT+RU3BtQbfVc73Eg2nP+5cAfnuawf+LuEzHGb2w/C2OMMXWymoUxxpg6WbIw\nxhhTJ0sWxhhj6mTJwhhjTJ0sWRhjjKmTJQtjjDF1smRhjDGmTpYsjDHG1MmShTHGmDr9P8Ucl1Pp\nrO0dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Triceps_skin_fold_thickness', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Triceps_skin_fold_thickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### serum_insulin\n",
    "餐后血清胰岛素（单位:mm）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kqZUBIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJadRYQSa5LciDJHX1tpyXZnOSe5vXU\nvveuTrI7yd1JLu6qLknSYLrcgvggcMlRbVcBW6pqKbClmSfJMmAVcG6zzLuSzOuwNknSJDoLiKr6\nEvC9o5pXAhua6Q3ApX3t11fVoaraA+wGVnRVmyRpctN9DOLMqtoH0Lye0bQvAO7v6zfWtEmShmSm\nHKROS1u1dkzWJNmeZPvBgwc7LkuS5q7pDoj9SeYDNK8HmvYxYFFfv4XA3rYVVNX6qhqtqtGRkZFO\ni5WkuWy6A2ITsLqZXg3c0Ne+KskpSZYAS4Ft01ybJKnPSV2tOMnHgGcApycZA94AXAtsTHIFcB9w\nGUBV7UqyEbgTOAysraojXdUmSZpcZwFRVS88xlsXHaP/OmBdV/VIko7PTDlILUmaYQwISVIrA0KS\n1MqAkCS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrQwISVIrA0KS\n1MqAkCS16uyBQWr30a33DdTvRU85u+NKJGlibkFIkloNZQsiyb3Aj4EjwOGqGk1yGvD3wGLgXuA/\nVtX3h1HfQzHoloEknSiGuYvpmVX13b75q4AtVXVtkqua+b8YTmnD564oScM2k3YxrQQ2NNMbgEuH\nWIskzXnDCogCPptkR5I1TduZVbUPoHk9Y0i1SZIY3i6mC6tqb5IzgM1Jvjnogk2grAE4+2x3r0hS\nV4ayBVFVe5vXA8CngRXA/iTzAZrXA8dYdn1VjVbV6MjIyHSVLElzzrQHRJJHJ3ns+DTwb4E7gE3A\n6qbbauCG6a5NkvSgYexiOhP4dJLxz/9oVd2U5GvAxiRXAPcBlw2hNklSY9oDoqq+DZzX0v5PwEXT\nXY8kqZ232pgDvKZC0kNhQEzCK6QlzVUGxAnOAJPUlZl0JbUkaQYxICRJrQwISVIrA0KS1MqAkCS1\nMiAkSa08zVW/5AV1kvq5BSFJamVASJJaGRCSpFYGhCSplQepddw8mC3NDQaEhs7AkWYmdzFJklq5\nBaHOeCty6cQ24wIiySXA24F5wPuq6tohl6QZYpDAcTeUNHVmVEAkmQf8LfAcYAz4WpJNVXXncCuT\npo/HZDRTzKiAAFYAu6vq2wBJrgdWAgaEBjKs3VrD+M96qr+rgaOjzbSAWADc3zc/BjxlSLVIA5tL\nx1uGsYUzrK2qYezWnElbkDMtINLSVr/SIVkDrGlmf5Lk7t/g804HvvsbLD+bOTYTm3Xj8+KpXd3p\nLx7C+Ezxd+jyM3/j35/f8Ls+fpBOMy0gxoBFffMLgb39HapqPbB+Kj4syfaqGp2Kdc02js3EHJ+J\nOT4TO1HGZ6ZdB/E1YGmSJUkeDqwCNg25Jkmak2bUFkRVHU7yCuBmeqe5XldVu4ZcliTNSTMqIACq\n6jPAZ6bp46ZkV9Us5dhMzPGZmOMzsRNifFJVk/eSJM05M+0YhCRphpiTAZHkkiR3J9md5Kph1zMM\nSRYl+UKSu5LsSnJl035aks1J7mleT+1b5upmzO5OcvHwqp8eSeYl+XqSG5t5x6ZPkscl+USSbza/\nR09zjHqS/Fnz7+qOJB9L8ogTcWzmXED03c7j94FlwAuTLBtuVUNxGHhNVT0JeCqwthmHq4AtVbUU\n2NLM07y3CjgXuAR4VzOWs9mVwF19847Nr3o7cFNV/UvgPHpjNefHKMkC4FXAaFU9md4JN6s4Acdm\nzgUEfbfzqKqfA+O385hTqmpfVd3aTP+Y3j/uBfTGYkPTbQNwaTO9Eri+qg5V1R5gN72xnJWSLAT+\nAHhfX7Nj00jyz4CnA+8HqKqfV9UPcIzGnQQ8MslJwKPoXc91wo3NXAyIttt5LBhSLTNCksXA+cBW\n4Myq2ge9EAHOaLrNtXF7G/DnwAN9bY7Ng54AHAQ+0OyGe1+SR+MYUVXfAd4E3AfsA35YVZ/lBByb\nuRgQk97OYy5J8hjgk8Crq+pHE3VtaZuV45bkecCBqtox6CItbbNybPqcBPwu8O6qOh/4Kc0uk2OY\nM2PUHFtYCSwBzgIeneTyiRZpaZsRYzMXA2LS23nMFUlOphcOH6mqTzXN+5PMb96fDxxo2ufSuF0I\n/GGSe+ntgnxWkg/j2PQbA8aqamsz/wl6geEYwbOBPVV1sKp+AXwK+D1OwLGZiwHh7TyAJKG3//iu\nqnpL31ubgNXN9Grghr72VUlOSbIEWApsm656p1NVXV1VC6tqMb3fj89X1eU4Nr9UVf8I3J/kt5um\ni+jdlt8x6u1aemqSRzX/zi6id4zvhBubGXcldde8nccvXQi8BLg9yc6m7XXAtcDGJFfQ+0W/DKCq\ndiXZSO8/gcPA2qo6Mv1lD5Vj86teCXyk+UPr28Af0/ujc06PUVVtTfIJ4FZ63/Xr9K6cfgwn2Nh4\nJbUkqdVc3MUkSRqAASFJamVASJJaGRCSpFYGhCSplQEhSWplQEgdS/InSf7TFK/zg0n+qJl+3xy9\nI7E6NuculJOOJclJVXV4qtdbVe+Z6nUetf6Xdrl+zV1uQWjWSfLoJP8ryTeaB7a8IMkFSb6YZEeS\nm/vuiXNLkr9O8kXgyv6/zJv3f9K8PqNZfmOSbyW5NsmLk2xLcnuSJ05QzzVJXtv3eW9slvtWkn/d\ntJ/btO1McluSpUkWJ7mjbz2vTXJNy/pvSTI6Xm+Sdc13/2qSM6dmVDUXGRCajS4B9lbVec0DW24C\n3gn8UVVdAFwHrOvr/7iq+jdV9eZJ1nsevYcI/Q6925ScU1Ur6D0z4pXHUd9JzXKvBt7QtP0J8Paq\nWg6M0ruB20PxaOCrVXUe8CXgZQ9xPZK7mDQr3Q68KckbgRuB7wNPBjb37p3GPHr36R/39wOu92vj\n9/NP8n+Bz/Z93jOPo77xO+fuABY3018BXt88qOhTVXVPU+vx+jm97zy+/uc8lJVIYEBoFqqqbyW5\nAHgu8F+BzcCuqnraMRb5ad/0YZot6+ZOnA/ve+9Q3/QDffMPcHz/lsaXOzK+XFV9NMlWek+xuznJ\nS4Fv8atb+Y8YYN2/qAdvsPbL9UsPhbuYNOskOQv4WVV9mN6TvZ4CjCR5WvP+yUnOPcbi9wIXNNMr\ngZM7LpempicA366qd9C7/fO/AvYDZyT5rSSnAM+bjlqkcf51odnod4C/SfIA8AvgT+ltGbwjyT+n\n93v/NqDtNu/vBW5Iso3eg+V/2tKnCy8ALk/yC+Afgb+qql8k+St6j4LdA3xzmmqRAG/3LUk6Bncx\nSZJauYtJmiJJXk/zlLA+H6+qdW39pZnOXUySpFbuYpIktTIgJEmtDAhJUisDQpLUyoCQJLX6/y3s\nawdfbAyxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.serum_insulin, kde = False)\n",
    "plt.xlabel('serum_insulin')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "serum_insulin与标签之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w87WK59ZnsrXWydZaJ79fkctz6zNDfyjojMEehEDTV08Tsz4L85XKyko+//wz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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='serum_insulin', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('serum_insulin', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BMI\n",
    "体重指数（体重（公斤）/ 身高（米）^2）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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anfZCc5fk6cBngGuq6qFh52mjqh6v3pD6BODsJM8fdqbpJHkZsKeqtgw7yyydX1Vn0Zum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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.BMI, kde = False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+3+7Abf+WA15jDPx7VxivpUfTu3cfXn5lnGV9CbWaEgpnAncbY/4N/A1oD3wO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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='BMI', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BMI=0？\n",
    "为缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ff1a58>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mC/t7uofNa7jxi2dccls2PncNY6Pn56k4f5UxmQeNk10trxrzqZR+RV3qqFdF+fHWmxkP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\nRBTURURK5P8DING7dFzFL7YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF = train.groupby(['BMI', 'Target'])['BMI'].count().unstack('Target').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diabetes_pedigree_function，糖尿病家系作用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AdQArVnin7WLW7x3X4F3X0iAMZQSRZH+a5PCRqrqyLb4vyRHt+iOAXV2frar1VTVeVeNj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\n5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Diabetes_pedigree_function，糖尿病家系作用\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Diabetes_pedigree_function, kde = False)\n",
    "plt.xlabel('Diabetes_pedigree_function')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x278d0550>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JCCHE4NFsEyGEqCBy3kIIUUHKTBW8HvglcJiZ9ZjZh1qvlhBCiCLG1RNwzp09HIoIIYQo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\nTLDNa/CNi6XAkmC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9zrnVqXiz5LP0KmJL4v8Y4Pjo6BPpZKZfkvihWxj4zcnkW5nxE2xx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\nqCBy3kIIUUHkvIUQooLIeQshRAX5/7bnyS6URI1VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Diabetes_pedigree_function', 'Target'])['Diabetes_pedigree_function'].count().unstack('Target').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TwElVdSSwADglyXGMZy8AHwTunrQ8rn0AvLaqFkw69HBce/kb4LqqegVwJL3fz9j1UlWr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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Age, kde = False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26113320>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mAUvC87KKRCjVpTtBivRaxVxYdCbw58AvzWx9KPs/JIn8YTO7BtgOXF6ZEEVEpBgFE7q7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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Age', 'Target'])['Age'].count().unstack('Target').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ab0ab38>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x8U7yfTR/viF/TH/0Kwi5Awpy4VQEF8+cxXbTxt55myhSt6xTzD/X/467nSZD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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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us59R6sPOdlX7OmBlksclORxYBtw4hPom1V+A6R3Aq6vq5+Memts+VNXI/gFeSfdJ9n8D\n7xx2PXtQ9x/S/TftG8Ct/Z9XAk+mO7tgc3+7cNi1TrE/LwKu7u+Pah9+H9jY/5v8G7D/qPUFeDfw\nTeB24F+Ax41KH4BP0n228BDdaPfM3dUOvLN/398FvGLY9e+mD3fTzcXveJ9/dBh98JuxktS4UZ66\nkSRNgUEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g17zSpKl45d5Hdd+Q5KBL6qc5PVJ/mHQ15FGiUEv\n7aF+dci5OtZ8WH5XI86g13y0IMma/mINVybZZ/yDSU5Lclt/gY33TaH9DUm+leSLdGv07FKSi5N8\nNMmX++ec3Le/Psmnk/w7cG3f9tdJvt7X+e6+bd8kn03yX30dr+3bz09yZ7/vBeOO9Zpxx/5Zf/ui\ndBemuQy4rW87I8mNSW5N8jF/AWhPzNnIRNoDzwDOrKqvJrkI+MsdDyQ5CHgfcDTwQ+DaJKfQrRMy\nUfsGuqUBjgZ+DFwP3DLJ8ZcCLwSeBlyf5Ol9+/HAc6rqB0leRrc+yTF0KxGuS/ICYBFwf1W9qq/3\niUkWAn8MHFFVNf4qQ7txDN0FK+5J8kzgtcAJVfVQkg8DrwP+eQqvIxn0mpfuq6qv9vcvobty0g5/\nANxQVWMASS6lu2hI7aKdndo/BfzeJMe/oqoeATYn+TZwRN9+XVXtuLDEy/o/O35p7EcX/F8GLuj/\nR3F1VX25n+r5BXBhks8CV0/h7+DG6i5IAd0CZUcDX+/WK+MJjNYqlBoyg17z0c4LMI3fnmgd7921\nT/R60z3+gzsd771V9bHfKiQ5mm6Ruvcmubaq/j7JMXSBvRJ4E/BiuvXJ9+qfE+Cx415m52Otqarz\n9rAfEuAcveanw5Ic398/DfjKuMc2AC9MckA/T30a8MVJ2l+U5Mn9NQBOncLxT02yV5Kn0V196q4J\n9vk88Mb+4jEkOTjJ4n5q6edVdQndFZ+e1+/zxKq6BngL3UqZAPfSjdShu2jIY3ZRz3rgNUkW98da\nmOQpU+iHBDii1/y0CViV5GN0S9R+BPgj6C4pl+Q8urn2ANdU1VqA3bT/HfA1uiVkb6a7cPbu3EX3\nS+JA4M+r6hf9lMn/q6pr+7nzr/WP/Qw4A3g68P4kj9AtV/sXwO8Aa5M8vq/trf3L/FPffiNdmD/I\nBKrqziR/S/e5w179654NfGeSfkgALlMsjZfkYrq59SuHXYs0U5y6kaTGOXWjR6V0F2veeb7+01X1\n+iGUI80qp24kqXFO3UhS4wx6SWqcQS9JjTPoJalxBr0kNe7/AL0QYFN0dzhqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mdipBrU1V9TmGH+2Q5LXAl6rqD0b7tF8vpqqWPT/2Y2kFdf4M8M6q+r1x1jPiVcAVwJcX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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0ktQ4g16SGmfQS1LjPL2yQX6xSlI/j+glqXEGvSQ1zqCXpMYZ9JLUOD+MvYD5oa10YTDo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\nGfSS1DiDXpIaZ9BLUuP+Pw7b+p9RKB7dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p4ADw1yPIJw2F0y867lTVPwJrgak5x34I7ATuZLBqpTSRHKnruJPkAuAEBosnnTznrj8D\n/q6qXhssYClNHktdx4tDc+ow+OKVjVX1ztzyrqpd+K4XTThXaZSkhjinLkkNsdQlqSGWuiQ1xFKX\npIZY6pLUEEtdkhpiqUtSQyx1SWrI/wHY+P3fGlIICAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rgP9cPV8Ejkly4tNd6AQNcwyaVlW3Ad9Zoknr58Awx6B5VbWnqu7qpr8P7OTH76hv+lwY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\nl6TGGfSS1Lj/B20EITOd5U3rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kNc6gl6TGGfSS1DiDXse9JO9NUkneMu5apFEw6KXeyTlfpXcSmNQcg17Hte5aSpcB19EF\nfZLXJLmlu5b43UnuSfK+7rGLkvxTdyG0Lx05nV2aZAa9jndX07uO+78BTyd5O/DLwArgp+mdzXgp\nvHDtpb8C3ldVFwG3ApvHUbR0LEb24eDSlNhA79LL0LtQ2wbgJOCzVfUj4LtJvtw9fi5wAXBf7/Il\nnEDvErTSRDPoddzqrp9yBXBBkqIX3MWL18B52VOAXVV16atUojQUTt3oePY+4G+r6s1VtaKqltP7\npKangF/p5urPAC7v1t8NzCR5YSonyfnjKFw6Fga9jmcbePne+530Pihilt5liz9O74qpz1TVD+n9\ncviTJN8AHqZ3jXFponn1SmkeSU6pqh900zsP0Ps0qu+Ouy5pMZyjl+Z3d/dhMa8F/tiQ1zRzj16S\nGuccvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrc/wEJxwB2HMFO6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ny62bPksq7ABe1316fRHwnaq6b6ELHaORY05yDvBh4LVTPLsbNnLMVXVuVa2uqtXAvwK/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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in train.columns:\n",
    "    sns.distplot(train[feature],kde = False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 适当的特征工程\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 导入必要的工具包\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "train = pd.read_csv('pima-indians-diabetes.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "粗看数据集没有缺失值但该数据已知存在缺失值，某些列中存在的缺失值被标记为0.通过这些列中指标的定义和相应领域的常识可以证实上述观点，譬如体重指数和血压两列中的0作为指数数值来说是无意义的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数值型特征的基本统计量\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从结果中我们可以看到很多列的最小值为0，而在一些特定列代表的变量中，0值并没有意义，这就表明该值无效或为缺失值。 具体来说，下列变量的最小值为0时数据无意义：\n",
    "\n",
    "血浆葡萄糖浓度\n",
    "\n",
    "舒张压\n",
    "\n",
    "肱三头肌皮褶厚度\n",
    "\n",
    "餐后血清胰岛素\n",
    "\n",
    "体重指数\n",
    "\n",
    "在Pandas的DataFrame中，通过replace()函数可以很方便的将我们感兴趣的数据子集的值标记为NaN。\n",
    "标记完缺失值之后，可以利用ishull()函数将数据集中所有的NaN值标记为True，然后就可以得到每一列中缺失值的数量类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         0\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0,np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对缺失值较多的特征，新增一个特征，表示这个特征是否缺失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>Triceps_skin_fold_thickness_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Triceps_skin_fold_thickness  Triceps_skin_fold_thickness_Missing\n",
       "0                         35.0                                    0\n",
       "1                         29.0                                    0\n",
       "2                          NaN                                    1\n",
       "3                         23.0                                    0\n",
       "4                         35.0                                    0\n",
       "5                          NaN                                    1\n",
       "6                         32.0                                    0\n",
       "7                          NaN                                    1\n",
       "8                         45.0                                    0\n",
       "9                          NaN                                    1"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Triceps_skin_fold_thickness缺失值较多，干脆就开一个新的字段，表明是缺失值还是不是缺失值\n",
    "train['Triceps_skin_fold_thickness_Missing'] = train['Triceps_skin_fold_thickness'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['Triceps_skin_fold_thickness','Triceps_skin_fold_thickness_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c408940>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nRMRzUFxXGvgBRXCYmdk+qNoRxN+0hQNARDxLcYU4MzPbR1UbEPtJOrjtSRpBVDv6MDOz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\nZmaW9f8BuRPnmYYvFr4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.countplot(x='Triceps_skin_fold_thickness_Missing', hue='Target', data = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>serum_insulin_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>88.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>543.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   serum_insulin  serum_insulin_Missing\n",
       "0            NaN                      1\n",
       "1            NaN                      1\n",
       "2            NaN                      1\n",
       "3           94.0                      0\n",
       "4          168.0                      0\n",
       "5            NaN                      1\n",
       "6           88.0                      0\n",
       "7            NaN                      1\n",
       "8          543.0                      0\n",
       "9            NaN                      1"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# serum_insulin缺失值较多，干脆就开一个新的字段，表明是缺失值还是不是缺失值\n",
    "train['serum_insulin_Missing'] = train['serum_insulin'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['serum_insulin','serum_insulin_Missing']].head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c9369e8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jYjIwBVgyyLVJkpq0/JWjOyoirgc+BOwXEb3ApcBXgRsj4mzgCeCTAJm5IiJupPGVppuB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\neKjN9CAGwsUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='serum_insulin_Missing', hue='Target', data = train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从数据上观察，特征是否缺失好像和目标没什么关系\n",
    "\n",
    "因为特征是否缺失好像和目标没什么关系，感觉特征缺失是随机的，所以删除新增的特征，老老实实用中值填补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "train.drop(['serum_insulin_Missing','Triceps_skin_fold_thickness_Missing'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用中值填补缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                       0\n",
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "medians = train.median()\n",
    "\n",
    "# fillna()会将DataFrame中nan数据的数据填充为想要的数据，并返回填充后的结果。如果希望在原DataFrame中修改，则把inplace设置为True。\n",
    "train = train.fillna(medians)\n",
    "\n",
    "print(train.isnull().sum())  # isnull().sum()就更加直观了，它直接告诉了我们每列缺失值的数量。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\envs\\py2\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\py2\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "# 分离特征和目标\n",
    "y_train = train['Target']\n",
    "X_train = train.drop(['Target'], axis=1)\n",
    "\n",
    "# 用于保存特征工程之后的结果\n",
    "feat_names = X_train.columns\n",
    "\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化\n",
    "X_train = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征处理结果存为文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 存为csv格式\n",
    "X_train = pd.DataFrame(columns=feat_names, data = X_train)\n",
    "\n",
    "train = pd.concat([X_train, y_train], axis=1)\n",
    "\n",
    "train.to_csv('FE_pima-indians-diabetes_lpw.csv',index = False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  3.采用5折交叉验证，分别用log似然损失和正确率，对Logistic回归模型的正则超参数调优。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (1)采用log似然损失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log损失评估的交叉验证后的最佳得分：\n",
      "0.4760260032377403\n",
      "log损失评估的最佳超参数为：\n",
      "{'penalty': 'l1', 'C': 1}\n"
     ]
    }
   ],
   "source": [
    "# 导入必要的工具包\n",
    "# Logistic Regression + GridSearchCV 超参数调整 \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 读入数据\n",
    "train = pd.read_csv('FE_pima-indians-diabetes_lpw.csv')\n",
    "\n",
    "# 分离特征和目标\n",
    "y_train = train['Target']\n",
    "X_train = train.drop(['Target'], axis=1)\n",
    "\n",
    "# 保存特征名字以备后用\n",
    "feat_names = X_train.columns\n",
    "# 1.设置参数搜索范围\n",
    "penaltys = ['l1', 'l2']\n",
    "Cs = [0.001,0.01,0.1,1,10,100,1000]\n",
    "\n",
    "# 2.生成学习器实例\n",
    "lr_penalty = LogisticRegression(solver='liblinear')\n",
    "\n",
    "# 3.生成GridSearchCV的实例\n",
    "tuned_parameters = dict(penalty=penaltys,C=Cs)\n",
    "grid_log = GridSearchCV(lr_penalty, tuned_parameters, cv=5, scoring='neg_log_loss', n_jobs=4)  # 5折交叉验证，log似然损失\n",
    "# 4.调用GridSearchCV的fit方法\n",
    "grid_log.fit(X_train, y_train)\n",
    "# 打印训练结果\n",
    "print('log损失评估的交叉验证后的最佳得分：')\n",
    "\n",
    "print(-grid_log.best_score_)\n",
    "\n",
    "\n",
    "print('log损失评估的最佳超参数为：')\n",
    "\n",
    "print(grid_log.best_params_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "采用log似然损失为模型评价指标时：最佳超参数为C=1，采用L1正则。此时的负log损失最小，为0.476"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## （2）采用正确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正确率评估的交叉验证后的最佳得分:\n",
      "0.7747395833333334\n",
      "正确率的最佳超参数为：\n",
      "{'penalty': 'l2', 'C': 0.1}\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression + GridSearchCV 超参数调整 \n",
    "#encoding=utf-8\n",
    "\n",
    "\n",
    "# 1.设置参数搜索范围\n",
    "penaltys = ['l1', 'l2']\n",
    "Cs = [0.001,0.01,0.1,1,10,100,1000]\n",
    "\n",
    "# 2.生成学习器实例\n",
    "lr_penalty = LogisticRegression(solver='liblinear')\n",
    "\n",
    "# 3.生成GridSearchCV的实例\n",
    "tuned_parameters = dict(penalty=penaltys,C=Cs)\n",
    "grid_accuracy = GridSearchCV(lr_penalty, tuned_parameters, cv=5, scoring='accuracy', n_jobs=4)  #5折交叉验证，正确率评价指标\n",
    "# 4.调用GridSearchCV的fit方法\n",
    "grid_accuracy.fit(X_train,y_train)\n",
    "# 打印训练结果\n",
    "print('正确率评估的交叉验证后的最佳得分:')\n",
    "\n",
    "print(grid_accuracy.best_score_)\n",
    "\n",
    "print('正确率的最佳超参数为：')\n",
    "\n",
    "print( grid_accuracy.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "采用正确率为模型评价指标时：最佳超参数为C=0.1，采用L2正则。此时正确率最高为0.77474"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 不足之处"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "在采用Logistic RegressionCV进行超参数调优的时候，对fit后如何获取评价值和最优超参数不太清楚，感觉还不如用Logistic+GridSearchCV方便，直接调用GridSearchCV.best_score_/.best_params_就可以了。"
   ]
  }
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